Full Stack AI Engineer

Job not on LinkedIn

🔥 6 minutes ago

🇦🇷 Argentina – Remote

💵 $36k - $60k / year

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 0%

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CareTria

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

💊 Pharmaceuticals

🏥 Healthcare

📦 Logistics

💰 Private Equity Round on 2025-07

Pharmaceuticals • Healthcare • Logistics

CareTria is a healthcare services company that operates patient-centric specialty pharmacy brands and an integrated platform connecting pharmaceutical manufacturers, specialty providers, and patients. It provides end-to-end patient access solutions (benefit investigation, electronic prior authorization, co-pay support, HUB integration), specialty pharmacy fulfillment (licensed across all 50 states, direct-to-patient cash programs, PAP/bridge/free trial support, white-label pharmacy), third-party logistics and samples management (3PL, warehousing, kitting, compliant sample distribution), plus provider engagement and eCommerce tools. CareTria’s platform emphasizes automation to accelerate therapy initiation (real-time benefit investigations in ~60 seconds), improve conversion rates, and reduce operational overhead for pharma partners and providers.

📋 Description

• Build AI applications for healthcare workflows • Design and build RAG pipelines, including document ingestion, chunking, embeddings, vector storage, and retrieval quality testing • Integrate LLM provider APIs including Azure AI Foundry, Amazon Bedrock, and OpenAI, balancing quality, cost, and latency • Develop agentic workflows using LangChain and LangGraph, including tool calling, multi-step orchestration, and persistent conversation state • Build real-time voice agents using WebSockets, streaming speech-to-text and text-to-speech pipelines, and platforms such as Retell • Develop application features and backend services using Python 3.12+ and JavaScript/Node.js • Design REST APIs and asynchronous processing workflows using BullMQ, RabbitMQ, or Amazon SQS • Implement structured outputs, validation, retries, and fallback behavior for dependable AI features • Establish tracing, evaluation, and monitoring to assess model behavior, diagnose failures, and improve performance • Containerize services with Docker and Docker Compose; contribute to AWS deployments and CI/CD pipelines • Collaborate on sprint planning, code reviews, and technical decisions while applying healthcare privacy and security practices • Deliver reliable AI features with measurable retrieval quality, responsive voice interactions, and clear system visibility • Balance model quality, cost, latency, and context limits while maintaining secure, understandable, production-ready software

🎯 Requirements

• Strong practical experience building RAG systems, including vector search, embeddings, chunking strategies, and retrieval tradeoffs • Hands-on experience integrating LLM provider APIs and shipping AI features into production • Proficiency in Python and JavaScript/Node.js, including API development and service integrations • Experience with LangChain and LangGraph for orchestrating stateful LLM workflows • Experience building real-time voice applications, including streaming, turn-taking, interruption handling, and latency optimization • Familiarity with message queues and reliable background processing • Working knowledge of Docker, Docker Compose, Git, and GitHub • Familiarity with CI/CD tools such as AWS CodePipeline or equivalent • Experience working in an iterative development environment using Jira and sprint planning • Awareness of HIPAA and responsibilities involved in handling protected health information (PHI) • Clear communication, sound technical judgment, and ability to turn loosely defined requirements into maintainable software • Additional valued experience with Parse and MongoDB • Additional valued experience with AWS services such as ECS, Lambda, and Application Load Balancer • Additional valued experience with healthcare software or applications involving sensitive data • Additional valued experience with multi-agent coordination and long-running workflows with checkpointing, memory, and persistence • Additional valued experience with prompt engineering, schema-constrained generation, and function/tool definitions • Additional valued experience with LLM evaluation, observability, and testing of non-deterministic systems • Additional valued experience mitigating hallucinations, prompt injection, and unsafe tool use through validation and guardrail design

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